import torch
import torch.nn as nn

class SCM(nn.Module):
    """Structural Causal Model with Dual Tower Architecture"""
    def __init__(self, n_users, n_items, config):
        super().__init__()
        self.user_embed = nn.Embedding(n_users, config.user_dim)
        self.item_embed = nn.Embedding(n_items, config.item_dim)
        
        self.user_network = nn.Sequential(
            nn.Linear(config.user_dim, config.hidden_dim),
            nn.ReLU(),
            nn.Dropout(config.dropout)
        )
        
        self.item_network = nn.Sequential(
            nn.Linear(config.item_dim, config.hidden_dim),
            nn.ReLU(),
            nn.Dropout(config.dropout)
        )

    def forward(self, users, items):
        user_emb = self.user_network(self.user_embed(users))
        item_emb = self.item_network(self.item_embed(items))
        return torch.matmul(user_emb, item_emb.T)

class Generator(nn.Module):
    """GAN Generator for Counterfactual Data"""
    def __init__(self, config):
        super().__init__()
        self.net = nn.Sequential(
            nn.Linear(config.noise_dim + config.hidden_dim * 2, 256),
            nn.LeakyReLU(0.2),
            nn.Linear(256, config.hidden_dim),
            nn.Tanh()
        )

    def forward(self, z, user_emb, item_emb):
        inputs = torch.cat([z, user_emb, item_emb], dim=1)
        return self.net(inputs)

class Discriminator(nn.Module):
    """Dual Discriminator Architecture"""
    def __init__(self, config):
        super().__init__()
        # Causal fidelity discriminator
        self.D1 = nn.Sequential(
            nn.Linear(config.hidden_dim, 128),
            nn.LeakyReLU(0.2),
            nn.Linear(128, 1),
            nn.Sigmoid()
        )
        
        # Bias correction discriminator
        self.D2 = nn.Sequential(
            nn.Linear(config.hidden_dim, 128),
            nn.LeakyReLU(0.2),
            nn.Linear(128, 1)
        )

    def forward(self, x):
        return self.D1(x), self.D2(x)

class DRRecommender(nn.Module):
    """Dual Robust Recommendation System"""
    def __init__(self, scm, generator, discriminator, config):
        super().__init__()
        self.scm = scm
        self.generator = generator
        self.discriminator = discriminator
        self.config = config

    def compute_loss(self, real_data, fake_data, popularity):
        # Dual robust loss calculation
        # Implementation of equation (7) from the paper
        pass
